Technical Field
A “Stochastic Clustering-Based Network Generator” provides various automated techniques that enable rapid formation of an interconnected hierarchical network structure from an arbitrary number of agents via an iterative turn-based coalescence process.
Background
Conventional stochastic coalescence techniques typically involve processes that act via a continuous-time process where the coalescence rate of two clusters with given masses x,y (which can be either discrete or continuous) is dictated up to re-scaling by a rate kernel K. One well-known example of such techniques, referred to as “Kingman's coalescent,” corresponds to the kernel K(x,y)=1 and has been intensively studied in mathematical population genetics. In general, Kingman's coalescent can be obtained as the continuous/infinite limit of a process where at each time t there is a population of N individuals, and each individual picks a parent at random from the previous generation of individuals, located in time t+1/N. Other examples of highly studied rate kernels include “Aldous's continuum random tree” which provides the additive coalescent K(x,y)=x+y, and “Erdos-Renyi random graphs” which provide the multiplicative coalescent K(x,y)=xy. These types of classical stochastic coalescence processes are generally understood to function as an asynchronous series of individual merges whose occurrences are governed by independent exponentials.